Adaptive Kalman filtering for INS GPS

After reviewing the two main approaches of adaptive Kalman filtering, namel y, innovation-based adaptive estimation (IAE) and multiple-model-based adap tive estimation (MMAE), the detailed development of an innovation-based ada ptive Kalman filter for an integrated inertial navigation system/global pos itioning system (INS/GPS) is given. The developed adaptive Kalman filter is based on the maximum likelihood criterion for the proper choice of the fil ter weight and hence the filter gain factors. Results from two kinematic fi eld tests in which the INS/GPS was compared to highly precise reference dat a are presented. Results show that the adaptive Kalman filter outperforms t he conventional Kalman filter by tuning either the system noise variance-co variance (V-C) matrix 'Q' or the update measurement noise V-C matrix 'R' or both of them.